[DOCS] Add high-level guide for kNN search

docs/reference/mapping/types/dense-vector.asciidoc

@@ -56,8 +56,7 @@ It is not possible to store multiple values in one `dense_vector` field.
 
 experimental::[]
 
-A _k-nearest neighbor_ (kNN) search finds the _k_ nearest
-vectors to a query vector, as measured by a similarity metric.
+include::{es-repo-dir}/search/search-your-data/run-knn-search.asciidoc[tag=knn-def]
 
 Dense vector fields can be used to rank documents in
 <<query-dsl-script-score-query,`script_score` queries>>. This lets you perform
@@ -92,11 +91,13 @@ PUT my-index-2
 efficient kNN search. Like most kNN algorithms, HNSW is an approximate method
 that sacrifices result accuracy for improved speed.
 
-NOTE: Indexing vectors for approximate kNN search is an expensive process. It can take
-substantial time to ingest documents that contain vector fields with `index`
-enabled.
+//tag::dense-vector-indexing-speed[]
+NOTE: Indexing vectors for approximate kNN search is an expensive process. It
+can take substantial time to ingest documents that contain vector fields with
+`index` enabled.
+//end::dense-vector-indexing-speed[]
 
-NOTE: Dense vector fields cannot be indexed if they are within
+Dense vector fields cannot be indexed if they are within
 <<nested, `nested`>> mappings.
 
 [role="child_attributes"]

docs/reference/search/knn-search.asciidoc

@@ -68,10 +68,12 @@ The kNN search API performs a k-nearest neighbor (kNN) search on a
 <<dense-vector,`dense_vector`>> field. Given a query vector, it finds the _k_
 closest vectors and returns those documents as search hits.
 
+//tag::hnsw-algorithm[]
 {es} uses the https://arxiv.org/abs/1603.09320[HNSW algorithm] to support
 efficient kNN search. Like most kNN algorithms, HNSW is an approximate method
 that sacrifices result accuracy for improved search speed. This means the
 results returned are not always the true _k_ closest neighbors.
+//end::hnsw-algorithm[]
 
 [[knn-search-api-path-params]]
 ==== {api-path-parms-title}